Executive functions and language domains are vulnerable to Social Determinants of Health: A cognitive analysis from the PROMOTE trial
Bibliographic record
Abstract
BACKGROUND: Social determinants of health (SDH) frame a complex interplay between environmental factors that increase the risk of many health conditions, including dementia. However, it is unclear whether SDH impairs cognitive domains differently. This study aimed to investigate the impact of SDH across distinct cognitive domains in a South American population, expanding current understanding predominantly based on European and North American cohorts. METHOD: Baseline data from the PROMOTE trial, conducted in a Brazilian cohort, was collected between 2023 and 2022. Participants were clinically evaluated and underwent the Montreal Cognitive Assessment (MoCA), with scores divided into subscores for six cognitive domains: Memory Index Score (MIS), Executive Index Score (EIS), Attention Index Score (AIS), Language Index Score (LIS), Visuospatial Index Score (VIS), and Orientation Index Score (OIS). SDH variables included years of education, ethnicity, family income, and neighborhood income. Regression models were used to evaluate the impact of SDH on total MoCA scores and subscores, adjusted for age and sex. RESULT: Data from 147 participants (mean age: 59 years; mean years of education: 13.2) were analyzed. The majority of participants were White (n = 139), while 12 were non-White (Tab. 1). Total MOCA scores were not associated with any SDH. However, regression analysis revealed that SDH had distinct associations with MoCA subscores. Years of education were significantly associated with EIS (β = 0.11, p-adjusted = 0.002). Additionally, lower family income was significantly associated with EIS (β = -0.32, p = 0.04). For LIS, neighborhood income was significantly associated (β = 0.03, p = 0.04). In contrast, no significant associations were found between racial origin and MoCA subscores. CONCLUSION: SDH independently influences specific cognitive domains. Education and family income significantly impact the Executive Index, while neighborhood income affects the Language domain. These findings underscore the critical role of socioeconomic factors in cognitive health and the nuanced ways in which SDH affect distinct cognitive domains. Racial origin did not show a significant influence, emphasizing the importance of targeting socioeconomic interventions to address disparities in cognitive outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".